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Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods

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  • Sanders, Dwight R.
  • Manfredo, Mark R.

Abstract

A battery of time series methods are compared for forecasting basis levels in the soybean futures complex: soybeans, soybean meal, and soybean oil. Specifically, nearby basis forecasts are generated with exponential smoothing techniques, autoregression moving average (ARMA), and vector autoregression (VAR) models. The forecasts are compared to those of the 5-year average, year ago, and no change methods. Using the 5-year average as the benchmark method, the forecast evaluation results suggest that alternative naive techniques may produce better forecasts, and the improvement gained by time series modeling is relatively small. In this sample, there is little evidence that the basis has become systematically more difficult to forecast in recent years.

Suggested Citation

  • Sanders, Dwight R. & Manfredo, Mark R., 2006. "Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 38(3), pages 513-523, December.
    • Handle: RePEc:cup:jagaec:v:38:y:2006:i:03:p:513-523_02
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    Cited by:

    1. Karen E. Lewis & Ira J. Altman & Mark R. Manfredo & Dwight R. Sanders, 2015. "Risk Premiums and Forward Basis: Evidence from the Soybean Oil Market," Agribusiness, John Wiley & Sons, Ltd., vol. 31(3), pages 388-398, June.
    2. Xiaojie Xu, 2017. "Short-run price forecast performance of individual and composite models for 496 corn cash markets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2593-2620, October.
    3. Lee, Yoonsuk & Brorsen, B. Wade, 2012. "Impacts of Permanent and Transitory Shocks on Optimal Length of Moving Average to Predict Wheat Basis," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 125001, Agricultural and Applied Economics Association.
    4. Welch, J. Mark & Mkrtchyan, Vardan & Power, Gabriel J., 2009. "Predicting the Corn Basis in the Texas Triangle Area," Journal of Agribusiness, Agricultural Economics Association of Georgia, vol. 27(1-2), pages 1-15.
    5. Onel, Gulcan & Karali, Berna, 2014. "Relative Performance of Semi-Parametric Nonlinear Models in Forecasting Basis," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169795, Agricultural and Applied Economics Association.
    6. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    7. Hatchett, Robert B. & Brorsen, B. Wade & Anderson, Kim B., 2010. "Optimal Length of Moving Average to Forecast Futures Basis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(1), pages 1-16.
    8. Yoonsuk Lee & B. Wade Brorsen, 2017. "Permanent shocks and forecasting with moving averages," Applied Economics, Taylor & Francis Journals, vol. 49(12), pages 1213-1225, March.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

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